Making the Most of Your Data: A Framework for Using Derived Fields in Customer Journey Analytics

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Introduction

One of the most exciting new features of Customer Journey Analytics (CJA) is the ability to create derived fields. This powerful tool allows users to manipulate and transform data from the Adobe Experience Platform (AEP) Connections level to CJA Data Views, providing more flexibility for comprehensive and interlinked approaches to CJA data asset outputs. By taking advantage of CJA's report-time processing, retroactive, and non-destructive capabilities, users can extract even more value from their data with derived fields.

In addition to the benefits mentioned above, derived fields can help users identify patterns and trends that were previously difficult to discern. This is because the ability to manipulate and transform data in this way enables more nuanced analyses and deeper insights. Furthermore, derived fields can be used to create custom components that are tailored to specific business needs, making it easier to track progress towards goals and objectives.

This blog post provides a detailed framework for decision-making when it comes to using derived fields in CJA. It covers key concepts to consider when manipulating data and creating custom derivative fields. The post is designed to help you get the most out of this powerful new feature and take your CJA implementation to the next level. By following the guidelines outlined in this post, users can unleash the full potential of derived fields and maximize the value of their CJA data.

How to Approach Derived Fields

Here are some steps you can follow to effectively approach derived fields in a sustainable way across varying use cases over the long term:

1. Set Your CJA Derived Field Goals: Before creating any derived fields, it is important to define your goals. What data insights are you trying to uncover with the derived field components? Do your derived field rules have a solid logical basis? What analysis dimensions do you want to enable or improve via the derived fields? Having a clear understanding of your achievable end goals, from the derived field component throughput to the final CJA report or analysis, will help you create relevant and valuable derived fields.

2. Rationalize Your AEP to CJA Connection Data Sources: After defining your goals, it is essential to have a background knowledge of the upstream Adobe Experience Platform (AEP) connection data sources that will serve as the basis for creating your CJA derived fields. These sources may include data from Adobe Analytics, other Adobe Experience Cloud solutions, or external data import sources. When creating rules for derived fields, keep in mind how those rules will operate across all datasets within your CJA Connections ecosystem. The underlying logic should be extensible and based on a sound data framework interpretation.

   Considerations around data availability and at which point to do data manipulation can also be a factor. Manipulations with derived fields, similar to data view component-level settings, are not present in the underlying AEP data, and therefore won't be available for use in Adobe Real-Time Customer Data Platform (RTCDP) or Adobe Journey Optimizer (AJO) utilization use cases. It is crucial to consider how consistent the data will flow into AEP over time and whether derived fields are a viable means of data manipulation as a stop-gap, mid-term, or long-term solution given their rule-based dependencies.

3. Understanding Your CJA Component Data Outputs: Before creating derived fields, it is important to have a thorough understanding of your holistic CJA end data. What types of derived field component data do you want as outcomes? What will be the end output data structures? Will there be any limitations of the component data when applied as derived fields, assuming maximum possible coverage? Understanding the scalability and flexibility of your available derived field data will help you create more accurate and effective derived fields.

4. Choose Your Derived Field Functions: Adobe Customer Journey Analytics provides a variety of functions for creating derived fields. These functions include a wide range of string manipulation and small-scale lookup functions. When creating derived fields, it is important to select functions that best fit the goals and structure of the data. Additionally, ensure that the rules will remain feasible across any anticipated variations and over time.

5. Test and Refine Derived Fields: After creating derived fields, it's important to test them using the derived fields interface “final output” preview functionality. This feature reviews the last 30 days of relevant data and allows you to refine rules directly within the rule builder as needed. Additionally, make sure to check that your derived fields provide the necessary insights over the entire CJA dataset and adjust them as necessary. If the data involved ages out of your CJA dataset over time, consider removing rules to maintain relevance. Remember that any Data View configuration changes made to CJA are reflected immediately upon saving, and are also enforced retroactively in any Data View where the derived field is included. As a CJA administrator, it is important to keep this in mind for governance purposes and communicate any changes to report users. Additionally, take advantage of the component CJA description field to document and communicate all settings and changes.

To summarize, we have successfully established and defined the scope of derived fields using a framework of established steps. This has ensured that all derived field rules function correctly and within their intended scope. We have also carefully examined the upstream AEP Connections data conditions to ensure that our derived fields are populated with accurate and reliable data.

Furthermore, we visualized the end state of the component in CJA and confirmed its functionality. We evaluated various functional methods for derived fields, considering factors such as accuracy, reliability, and scalability. Based on our evaluation, we selected the most effective methods for our derived fields.

We have completed testing and refinement of our execution of derived fields at report-time, ensuring seamless functionality without any issues. Through extensive testing and refinement, we have maximized the qualitative performance of derived fields, providing our end-users with accurate and reliable data.

Conclusion

To effectively work with derived fields, it is recommended that you follow the decision framework steps outlined earlier. This approach will help you gain a deeper understanding of your data design and flows, increasing your confidence in decision-making regarding the use of derived fields in Customer Journey Analytics.

Following the decision framework steps will not only help you gain a deeper understanding of your landed data, but also enable you to identify any gaps or inconsistencies in your data that may be candidates for derived field utilization. This can help refine your overall data collection processes, leading to better quality data and more accurate insights.

Moreover, using derived fields in Customer Journey Analytics can streamline your data analysis processes. By creating custom derivative fields tailored to your organization's specific needs and objectives, you can quickly and easily extract relevant insights for your business.

In summary, derived fields are a valuable tool for enhancing and improving data in Adobe Customer Journey Analytics without requiring re-processing. By using a structured and effective approach, you can leverage derived fields to build new and valuable data assets, address gaps or inconsistencies in your data, and streamline your data analysis processes.